Integrating Anthropogenic and Climatic Factors in the Assessment of the Caribbean Spiny Lobster (<i>Panulirus argus</i>) in Cuba: Implications for Fishery Management
Bibliographic record
Abstract
The Caribbean spiny lobster Panulirus argus , the most valuable Cuban fishery resource, is managed with a set of input and biological controls. The aim of this article was to integrate two indices related water and nutrients supply and tropical cyclones activity in the stock assessment, through internal estimation of the parameters of a spawning stock recruitment function in a statistical catch-at-age analysis. The population dynamics model allowed estimating key Reference Points for management at fixed levels of fishing mortality rate and environmental conditions. The results indicate that the reduction of recruitment and catches in the Cuban spiny lobster fishery could be a result of synergistic cumulative effects because of the anthropogenic reduction of nutrients supplies and the increase of the potential destructiveness of tropical cyclones since 1994, mainly from 2001 on. Although the degradation of the coastal habitat quality in Cuba is apparent and perhaps unavoidable, the assessment of the impact allows implementing management actions for the spiny lobster fishery sustainability. The implementation of annual TAC depending on the stock status, have maintained the fishery around the more conservative Reference Points F 40% and F 0.1 during the current unfavorable environment period.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".